Process industries including chemicals, oil and gas industries, pharmaceutics, mining, metals and pulp and paper play an important role in the Canadian economy. An unplanned shutdown of a large plant can cost several hundred thousand dollars.
Large amounts of process data of different type are collected during process operation and stored in process historians. Today, this valuable resource is not fully exploited because of the lack of dedicated tools and methods to extract reliable information from it. Detecting unintended deviations from normal operation or identifying the root cause of abnormal behavior becomes difficult with the ever-increasing amount and complexity of stored data.
Modern machine learning methods have the potential to support building robust and accurate process models able to predict the process behaviour and to diagnose abnormal process situations.
Professor Moncef Chioua’s group has an immediate opening for PhD position ("Machine learning driven decision support for the operation of process systems") on the intersection of data analytics, machine learning and process control.
The selected candidate will be working on the development of novel algorithms and computational tools to support the operation of process systems.
The work will be done in collaboration with industrial partners which will provide the selected candidate an opportunity to work on real-life case studies, to discuss with industrial practitioners and to benefit from the experience of industrial researchers in creating and deploying new technology.
